Environmental stressors such as temperature, humidity, vibration, and radiation can severely impact the performance and reliability of SSDs, particularly in edge, automotive, aerospace, and datacenter deployments. Capturing sensor data in the field and conducting accelerated lab experiments are challenging, as they are time-consuming, resource-intensive, and often destructive to hardware. Specialized setups, such as thermal chambers or vibration rigs, are also required, which is why few studies explore this area, and current storage management techniques like RAID, tiering, and deduplication do not consider environmental factors. Models to capture these impacts would open new research opportunities across various fields. However, accurately modeling these effects remains challenging due to, (1) the limited availability of experimental data, (2) the complex, domino-like impact of historical exposure, (3) the interrelated nature of environmental factors, such as temperature and humidity, which exhibit correlation, (4) different response of each type of NAND flash memory TLC, MLC, and SLC to environmental factors, and (5) the difficulty that analytical and simple machine learning models face in generalizing across devices, environments, and unseen combinations of stressors. We believe that LLMs may offer a transformative alternative to this complex problem, with embedded domain knowledge and reasoning capabilities, to facilitate prompt-based natural language interaction. We propose a hybrid framework that combines Chain-of-Thought prompting and Retrieval-Augmented Generation to guide LLMs using physical principles and prior experiments. It enables interpretable "what-if" analysis of SSD behavior under environmental changes. Our results show that the LLM can effectively model the impact of temperature, humidity, and vibration on SSD performance, producing tail latency and bandwidth predictions with minimal error. The code and data are available on GitHub at https://github.com/Damrl-lab/SSD_LLM.

Quantum Neural Networks (QNNs) harness quantum superposition and entanglement, offering promising advantages for machine learning tasks. However, noise in quantum computers frequently disrupts QNN training, wasting computational resources and extending queue times. This paper introduces the first QNN checkpointing framework to address this challenge. Through experiments on various quantum devices, we demonstrate that QNN behavior is fundamentally hardware-dependent, with the same model performing differently across platforms. This key finding shows that quantum checkpoints require additional metadata about hardware specifics and shot counts unique to quantum systems. Our framework requires minimal storage (only 186.6KB for a 100-qubit QNN) and negligible overhead, enabling frequent checkpointing to enhance training resilience and reproducibility in the NISQ era.

Transpilation, particularly noise-aware optimization, is widely regarded as essential for maximizing the performance of quantum circuits on superconducting quantum computers. The common wisdom is that each circuit should be transpiled using up-to-date noise calibration data to optimize fidelity. In this work, we revisit the necessity of frequent noise-adaptive transpilation, conducting an in-depth empirical study across five IBM 127-qubit quantum computers and 16 diverse quantum algorithms. Our findings reveal novel and interesting insights: (1) noise-aware transpilation leads to a heavy concentration of workloads on a small subset of qubits, which increases output error variability; (2) using random mapping can mitigate this effect while maintaining comparable average fidelity; and (3) circuits compiled once with calibration data can be reliably reused across multiple calibration cycles and time periods without significant loss in fidelity. These results suggest that the classical overhead associated with daily, per-circuit noise-aware transpilation may not be justified. We propose lightweight alternatives that reduce this overhead without sacrificing fidelity – offering a path to more efficient and scalable quantum workflows.